For autonomous driving, traversability analysis is one of the most basic and essential tasks. In this paper, we propose a novel LiDAR-based terrain modeling approach, which could output stable, complete and accurate terrain models and traversability analysis results. As terrain is an inherent property of the environment that does not change with different view angles, our approach adopts a multi-frame information fusion strategy for terrain modeling. Specifically, a normal distributions transform mapping approach is adopted to accurately model the terrain by fusing information from consecutive LiDAR frames. Then the spatial-temporal Bayesian generalized kernel inference and bilateral filtering are utilized to promote the stability and completeness of the results while simultaneously retaining the sharp terrain edges. Based on the terrain modeling results, the traversability of each region is obtained by performing geometric connectivity analysis between neighboring terrain regions. Experimental results show that the proposed method could run in real-time and outperforms state-of-the-art approaches.

翻译：对于自主驾驶而言，可通行性分析是最基础且最重要的任务之一。本文提出了一种新颖的基于LiDAR的地形建模方法，能够输出稳定、完整且精确的地形模型及可通行性分析结果。由于地形是环境的固有属性且不随视角变化，本方法采用多帧信息融合策略进行地形建模。具体而言，我们采用正态分布变换映射方法，通过融合连续LiDAR帧的信息来精确建模地形。随后，利用时空贝叶斯广义核推断与双边滤波提升结果的稳定性与完整性，同时保留尖锐地形边缘。基于地形建模结果，通过对相邻地形区域进行几何连通性分析，获取每个区域的可通行性。实验结果表明，所提方法能够实时运行，且性能优于现有先进方法。